The article discusses the limitations of Value-at-Risk in finance and proposes a method to estimate optimal portfolio allocations while considering the unknown distribution of data. The researchers aim to address the lack of diversification rewards and poor risk estimation behavior associated with VaR. By introducing a simple approach, they provide a way to minimize VaR while also deriving confidence intervals based on past observations.